computer graphics1

6.837 Introduction to Computer Graphics

Plan • Introduction • Overview of the semester • Administrivia • Iterated Function Systems (fractals) Overview

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Team • Lecturers – Frédo Durand – Barb Cutler

• Course secretary – Bryt Bradley

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Why Computer Graphics?

• • • • • • •

Movies Games CAD-CAM Simulation Virtual reality Visualization Medical imaging

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What you will learn in 6.837 • Fundamentals of computer graphics algorithms • Able to implement most applications just shown • Understand how graphics APIs and the graphics hardware work

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What you will NOT learn • Software packages – CAD-CAM – Photoshop and other painting tools

• Artistic skills • Game design • Graphics API – Although you will be exposed to OpenGL

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Plan • Introduction • Overview of the semester • Administrivia • Iterated Function Systems (fractals) Overview

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Overview of the semester • Ray Tracing – Quiz 1

• Animation, modeling, IBMR – Choice of final project

• Rendering pipeline – Quiz 2

• Advanced topics

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Ray Casting • For every pixel construct a ray from the eye – For every object in the scene • Find intersection with the ray • Keep if closest

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Ray Casting • For every pixel construct a ray from the eye – For every object in the scene • Find intersection with the ray • Keep if closest

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Ray Tracing • Shade (interaction of light and material) • Secondary rays (shadows, reflection, refraction

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Ray Tracing • Original Ray-traced image by Whitted Image removed due to copyright considerations.

• Image computed using the Dali ray tracer by Henrik Wann Jensen • Environment map by Paul Debevec

Image removed due to copyright considerations.

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Overview of the semester • Ray Tracing – Quiz 1

• Animation, modeling, IBMR – Choice of final project

• Rendering pipeline – Quiz 2

• Advanced topics

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Animation: Keyframing

Image adapted from: Lasseter, John. "Principles of Traditional Animation applied to 3D Computer Animation."

ACM SIGGRAPH Computer Graphics 21, no. 4 (July 1987): 35-44.

Squash & Stretch in Luxo Jr.'s Hop

Image adapted from: Lasseter, John. "Principles of Traditional Animation applied to 3D Computer Animation."

ACM SIGGRAPH Computer Graphics 21, no. 4 (July 1987): 35-44.

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Particle system (PDE) • Animation – Keyframing and interpolation – Simulation

Images removed due to copyright considerations.

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Rigid body dynamics • Simulate all external forces and torques f2 (t ) f1 (t )

p1b (t )

x (t )

pb2 (t )

v (t ) pb3 (t )

f3 (t )

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Modeling • Curved surfaces • Subdivision surfaces

Images removed due to copyright considerations.

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Image-based Rendering • Use images as inputs and representation •

E.g. Image-based modeling and photo editing Boh, Chen, Dorsey and Durand 2001

Input image

New viewpoint Overview

Relighting 18

Overview of the semester • Ray Tracing – Quiz 1

• Animation, modeling, IBMR – Choice of final project

• Rendering pipeline – Quiz 2

• Advanced topics

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The Rendering Pipeline Ray Casting • For each pixel

Rendering Pipeline • For each triangle

– For each object

Send pixels to the scene

– For each projected pixel

Project scene to the pixels

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The Rendering Pipeline • Transformations

FAR

z

z

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• Clipping

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y

x

NEAR

-n

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x EYE

x

z

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• Rasterization Courtesy of Leonard McMillan, Computer Science at the University of North Carolina in Chapel Hill. Used with permission

• Visibility

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Overview of the semester • Ray Tracing – Quiz 1

• Animation, modeling, IBMR – Choice of final project

• Rendering pipeline – Quiz 2

• Advanced topics

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Textures and shading

For more info on the computer artwork of Jeremy Birn see http://www.3drender.com/jbirn/productions.html „

Courtesy of Jeremy Birn. Used with permission.

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shadows

Image removed due to copyright considerations.

Image removed due to copyright considerations.

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Traditional Ray Tracing

Image removed due to copyright considerations.

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Ray Tracing+soft shadows

Image removed due to copyright considerations.

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Ray Tracing+caustics

Image removed due to copyright considerations.

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Global Illumination

Image removed due to copyright considerations.

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Antialiasing

Overview

Courtesy of Leonard McMillan, Computer Science at the University of North Carolina in Chapel Hill. Used with permission.

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Questions?

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Plan • Introduction • Overview of the semester • Administrivia • Iterated Function Systems (fractals) Overview

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Administrivia • Web: http://graphics.csail.mit.edu/classes/6.837/F03/ • Lectures – Slides will be online

• Office hours – Posted on the web

• Review sessions – C++, linear algebra

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Prerequisites • Not enforced • 18.06 Linear Algebra – Simple linear algebra, vectors, matrices, basis, solving systems of equations, inversion

• 6.046J Algorithms – Orders of growth, bounds, sorting, trees

• C++ – All assignments are in C++ – Review/introductory session Monday Overview

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Grading policy • Assignments: 40% – Must be completed individually – No late policy. Stamped by stellar.

• 2 Quizzes: 20% – 1 hour in class

• Final project: 40% – – – – –

Groups of 3, single grade for the group Initial proposal: 3-5 pages Steady weekly progress Final report & presentation Overall technical merit Overview

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Assignments • Turn in code AND executable • We will watch code style • Platform – Windows – Linux

• Collaboration policy: – You can chat, but code on your own

• No late policy Overview

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Project • Groups of 3 • Brainstorming – Middle of the semester

• Proposal • Weekly meeting with TAs • Report & presentation

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Plan • Introduction • Overview of the semester • Administrivia • Iterated Function Systems (fractals) Overview

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IFS: self-similar fractals • Described by a set of n transformations fi – Capture the self-similarity – Affine transformations – Contractions (reduce distances)

• An attractor is a fixed point

A = ∪ f i ( A) Image removed due to copyright considerations.

Image from http://spanky.triumf.ca/www/fractal-info/ifs-type.htm Overview 38

Example: Sierpinsky triangle • 3 transforms • Translation and scale by 0.5

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Rendering For a number of random input points (x0, y0) For j=0 to big number Pick transformation i (xk+1, yk+1) = fi (xk, yk) Display (xk, yk)

• Probabilistic application of one transformation

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Example: Sierpinsky triangle For a number of random input points (x0, y0) For j=0 to big number Pick transformation i (xk+1, yk+1) = fi (xk, yk) Display (xk, yk)

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Example: Sierpinsky triangle For a number of random input points (x0, y0) For j=0 to big number Pick transformation i (xk+1, yk+1) = fi (xk, yk) Display (xk, yk)

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Example: Sierpinsky triangle For a number of random input points (x0, y0) For j=0 to big number Pick transformation i (xk+1, yk+1) = fi (xk, yk) Display (xk, yk)

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Example: Sierpinsky triangle For a number of random input points (x0, y0) For j=0 to big number Pick transformation i (xk+1, yk+1) = fi (xk, yk) Display (xk, yk)

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Example: Sierpinsky triangle For a number of random input points (x0, y0) For j=0 to big number Pick transformation i (xk+1, yk+1) = fi (xk, yk) Display (xk, yk)

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Example: Sierpinsky triangle For a number of random input points (x0, y0) For j=0 to big number Pick transformation i (xk+1, yk+1) = fi (xk, yk) Display (xk, yk)

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Example: Sierpinsky triangle For a number of random input points (x0, y0) For j=0 to big number Pick transformation i (xk+1, yk+1) = fi (xk, yk) Display (xk, yk)

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Example: Sierpinsky triangle For a number of random input points (x0, y0) For j=0 to big number Pick transformation i (xk+1, yk+1) = fi (xk, yk) Display (xk, yk)

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Example: Sierpinsky triangle For a number of random input points (x0, y0) For j=0 to big number Pick transformation i (xk+1, yk+1) = fi (xk, yk) Display (xk, yk)

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Example: Sierpinsky triangle For a number of random input points (x0, y0) For j=0 to big number Pick transformation i (xk+1, yk+1) = fi (xk, yk) Display (xk, yk)

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Example: Sierpinsky triangle For a number of random input points (x0, y0) For j=0 to big number Pick transformation i (xk+1, yk+1) = fi (xk, yk) Display (xk, yk)

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Example: Sierpinsky triangle For a number of random input points (x0, y0) For j=0 to big number Pick transformation i (xk+1, yk+1) = fi (xk, yk) Display (xk, yk)

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Example: Sierpinsky triangle For a number of random input points (x0, y0) For j=0 to big number Pick transformation i (xk+1, yk+1) = fi (xk, yk) Display (xk, yk)

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Example: Sierpinsky triangle For a number of random input points (x0, y0) For j=0 to big number Pick transformation i (xk+1, yk+1) = fi (xk, yk) Display (xk, yk)

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Example: Sierpinsky triangle For a number of random input points (x0, y0) For j=0 to big number Pick transformation i (xk+1, yk+1) = fi (xk, yk) Display (xk, yk)

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Example: Sierpinsky triangle For a number of random input points (x0, y0) For j=0 to big number Pick transformation i (xk+1, yk+1) = fi (xk, yk) Display (xk, yk)

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Example: Sierpinsky triangle For a number of random input points (x0, y0) For j=0 to big number Pick transformation i (xk+1, yk+1) = fi (xk, yk) Display (xk, yk)

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Example: Sierpinsky triangle For a number of random input points (x0, y0) For j=0 to big number Pick transformation i (xk+1, yk+1) = fi (xk, yk) Display (xk, yk)

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Example: Sierpinsky triangle For a number of random input points (x0, y0) For j=0 to big number Pick transformation i (xk+1, yk+1) = fi (xk, yk) Display (xk, yk)

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Example: Sierpinsky triangle For a number of random input points (x0, y0) For j=0 to big number Pick transformation i (xk+1, yk+1) = fi (xk, yk) Display (xk, yk)

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Other IFS • The Dragon

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Application: fractal compression • Exploit the self-similarity in an image •

E.g. http://fractales.inria.fr/index.php?page=img_compression

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Assignment: IFS • Write a C++ IFS class • Get familiar with – vector and matrix library – Image library

• Due Wednesday at 11:59pm • Check on the web page http://graphics.lcs.mit.edu/classes/6.837/F03/ Overview

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Review/introduction session: C++ • Monday 7:30-9

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Questions?

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